Hyperprobe

About Hyperprobe

Hyperprobe is an observability tool that lets AI coding agents debug production issues without redeploying. It allows agents like Claude Code, Codex, or Cursor to drop read-only probes into a running service and capture variable state that was never recorded in logs or traces. The tool is built around an MCP (Model Context Protocol) integration and an SDK that backend teams add to their services.

Review

Hyperprobe tackles a specific pain point: production failures that don't reproduce locally and leave engineers guessing from incomplete telemetry. Instead of adding console.log statements and waiting on a deploy cycle, the tool gives AI agents direct access to in-memory state at the moment of failure. It launched this week and is currently in its early stages, with a small but growing set of language support and integration options.

Key Features

• Read-only probes that capture variable state from a running service without modifying its behavior
• MCP integration that plugs into Cursor, Claude Code, or Codex, allowing agents to debug using captured state as if they had a local reproduction
• In-process PII redaction that scrubs sensitive data before it leaves the application container
• Performance guardrails on CPU, memory, network, and latency that prevent data collection when the application can't spare compute resources
• Self-hosting option that keeps all captured data within a team's own VPC

Pricing and Value

Pricing details are not yet publicly defined. The product page lists "Payment Required" as a tag, but no specific tiers, subscription models, or per-seat costs have been published. Teams interested in using Hyperprobe would need to contact the founders directly for pricing information.

Pros

• Eliminates the log-and-redeploy loop by capturing variable state on demand
• Non-blocking probes add a 50MB memory footprint when firing, benchmarked at 3000 RPS
• Data redaction happens at capture time, with no sensitive data stored or processed by the platform
• Setup described as taking roughly 60 seconds: add the SDK and plug the MCP into an agent
• One reported user resolved a payments issue in 9.5 minutes that previously took 4 hours

Cons

• Language support is limited at launch; Go support is planned for end of month, and other languages beyond the initial set aren't confirmed
• The tool introduces a 50MB memory overhead when probes fire, which may matter for memory-constrained services
• Not well suited for teams that don't use AI coding agents as part of their debugging workflow, since the core value depends on agent integration

Hyperprobe fits teams that already rely on AI agents for production debugging and are frustrated by the gap between what logs capture and what actually happened in memory. It's less relevant for organizations that haven't adopted agent-based debugging or that run services where adding an SDK and MCP integration isn't feasible. The tool's usefulness will likely grow as its language support expands and more teams build debugging workflows around coding agents.

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